Learn how TensorFlow solves real, everyday machine learning problems

Explore how various companies from a wide variety of industries implement ML to solve their biggest problems. From healthcare to social networks and even ecommerce, ML can be integrated into your industry and company.

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CEVA’s NeuPro and CEVA-XM AI processors for Deep Learning and AI inferencing at the edge automatically convert TensorFlow trained networks for use in real-time embedded devices using the CEVA CDNN Compiler.

China Mobile has created a deep learning system using TensorFlow that can automatically predict cutover time window, verify operation logs, and detect network anomalies. This has already successfully supported the world’s largest relocation of hundreds of millions IoT HSS numbers.

Google uses TensorFlow to power ML implementations in products like Search, Gmail, and Translate, to aid researchers in new discoveries, and even to forge advances in humanitarian and environmental challenges.

The Liulishuo algorithm team first applied TensorFlow to its internal machine learning project in early 2016. This easy-to-use machine learning framework helped the team build an application to teach English.

Using TensorFlow NAVER Shopping automatically matches over 20 million newly registered products a day to around 5,000 categories in order to organize products systematically and allow easier searching for users.

Disease classification and segmentation were performed on retinal OCT images using TensorFlow. The three disease types were classified as either choroidal neovascularization, vitreous warts or diabetic retinal edema. After segmentation, Sinovation Ventures provided the boundary of the suspected lesions in the imaging.

VSCO used TensorFlow Lite to develop the “For This Photo” feature, which uses on-device machine learning to identify what kind of photo someone is editing and then suggest relevant presets from a curated list.